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Apprentice Machine Learning Testing Jobs (NOW HIRING)

Develop efficient workflows for training, validation, and testing, incorporating distributed ... Strong understanding of fundamental machine learning algorithms and neural network techniques.

Support experimentation, evaluation, testing, and continuous improvement of AI systems * Stay current with emerging AI, LLM, and machine learning technologies Required Experience / Ideal Background ...

Develop efficient workflows for training, validation, and testing, incorporating distributed ... Strong understanding of fundamental machine learning algorithms and neural network techniques.

CI/CD pipelines and regression testing. * AI/ML expertise: Machine learning fundamentals; Deep knowledge of state-of-the-art in any of the following: computer vision (preferred), natural language ...

They are seeking an experienced Machine Learning Engineer to design, implement, and optimize ... Develop efficient workflows for training, validation, and testing, incorporating distributed ...

Position requires experience in: 1. Building and executing end-to-end ML systems automating training, testing, and deploying Machine Learning models in cloud platforms 2. Machine learning frameworks ...

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... testing, and deployment in production. * Drive delivery for our product milestones, continually ...

Strong experience designing, building, training, and testing machine learning models end-to-end. * Proven ability to work with raw, unstructured, or incomplete data, including data collection ...

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Apprentice Machine Learning Testing information

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How much do apprentice machine learning testing jobs pay per hour?

As of Jun 3, 2026, the average hourly pay for apprentice machine learning testing in the United States is $19.36, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $21.15 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Apprentice Machine Learning Testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What kinds of projects or tasks can I expect to work on as an Apprentice Machine Learning Testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What does an Apprentice Machine Learning Testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

More about Apprentice Machine Learning Testing jobs
What cities are hiring for Apprentice Machine Learning Testing jobs? Cities with the most Apprentice Machine Learning Testing job openings:
What are the most commonly searched types of Machine Learning Testing jobs? The most popular types of Machine Learning Testing jobs are:
What states have the most Apprentice Machine Learning Testing jobs? States with the most job openings for Apprentice Machine Learning Testing jobs include:
What job categories do people searching Apprentice Machine Learning Testing jobs look for? The top searched job categories for Apprentice Machine Learning Testing jobs are:
Infographic showing various Apprentice Machine Learning Testing job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 89% Full Time, and 10% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $40,268 per year, or $19.4 per hour.

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Posted 3 days ago


Job description

Position Title: Machine Learning Engineer
Position Type: Full-time, On-Site
Location: Huntsville, AL
Clearance: Active TS
Description:
Waypoint's client is seeking a Machine Learning Engineer to support mission-critical efforts within a secure environment at the Missile and Space Intelligence Center. This role focuses on developing, integrating, and operationalizing machine learning solutions that support advanced analytics and intelligence capabilities.
The selected candidate will work across the full machine learning lifecycle, from building data pipelines and training models to deploying and monitoring production systems. This position requires a strong blend of software engineering and data science expertise, with a focus on scalability, performance, and system integration.
Responsibilities:
• Integrate machine learning systems into existing software architectures and enterprise platforms
• Design, build, and optimize data pipelines to support model training and inference
• Develop, test, and deploy machine learning models into production environments
• Manage transition from prototype to production, including deployment pipelines and monitoring solutions
• Monitor model performance, including handling model drift, rollback, and failure scenarios
• Conduct experiments and testing to evaluate and improve model accuracy and performance
• Write clean, maintainable, and testable code in Python and related technologies
• Collaborate with cross-functional teams to integrate ML capabilities into mission systems
• Utilize CI/CD pipelines and GitOps practices to support automated deployment and version control
• Support development in Linux and Windows environments
Required:
• Active TS clearance (with ability to obtain TS/SCI with CI Polygraph)
• Bachelor's degree in Computer Science, Mathematics, Statistics, Physics, or related technical field
• Minimum 12+ years of overall experience, including 1-3 years working with machine learning frameworks
• Strong programming skills in Python
• Experience with machine learning frameworks, libraries, and data modeling techniques
• Solid understanding of the machine learning lifecycle
• Experience working with SQL and NoSQL databases
• Experience working in Linux and Windows environments
• Familiarity with CI/CD pipelines and Agile development methodologies
• Understanding of software design and system integration principles
Desired:
• Active TS/SCI with CI Polygraph (desired)
• Experience working with large-scale (petabyte-level) datasets
• Experience supporting multi-INT analytics environments
• Experience deploying, monitoring, and scaling machine learning models in production
• Experience with tools such as Docker, Jupyter Notebooks, PostgreSQL, GitLab, and GitHub
• Experience implementing GitOps workflows
• Experience working in secure or classified environment